Current approaches for stereo imaging and metrology use template matching between the two stereo images. Template matching consists of selecting some region (template) of one image and, in effect, moving it around the other image until a sufficiently close match is found, at which point the two regions are assumed to be the same location seen from the two different cameras' perspectives. In the worst case scenario, every template-sized component of the first image must be compared with every portion of the second image. Even if there is some ability to restrict the processing region, such as use of the so-called epipolar imaging geometry, the general time involved with this procedure increases rapidly with the number of pixels in the image and template. In particular, the time increases as a factor of n2, where n2=(ni/nt)2, and where ni is the total number of pixels in the image and nt is the total number of pixels in the template. For example, when a template is 10×10 pixels and the image is 1,000×1,000 pixels, the number of comparisons will be on the order of (1,000,000/100)2, or 100,000,000 comparisons. A normal video rate is thirty frames per second, thereby requiring approximately three billion comparisons to be performed per second. Such a demand precludes processing stereo imagery in real-time using this approach with computing capabilities for currently available for many applications.
The resolution or accuracy of stereo metrology approaches is also limited. For example, they are highly dependent on the distance to the imaging cameras. This is due to the fact that an object of a given size subtends a smaller angle, and thus fewer pixels, at a greater distance. Therefore, for a single large object (such as a commercial vehicle), the accuracy of measurement may vary considerably from one part of an image to the next. Formally, the range to a point in object space is inversely proportional to the displacement in pixels (referred to as disparity) between the images of the object point in the pair of stereo images.
In addition, the current approach to stereo imaging and metrology also is dependent upon both cameras being able to see all interesting aspects of the target being measured. Components seen only by one camera cannot be measured using conventional stereo vision algorithms.